Archive for June, 2018

The term “big data” gets thrown around a lot, especially taking into account its importance for driving AI technology. Finding ways to build scalable systems that provide valuable insights into what you’re doing well and what you could be doing better is imperative to maintain a competitive edge. And, as big data, artificial intelligence and machine learning become more advanced and interconnected each year, these scalable systems become more and more valuable.

When PicsArt was founded in 2011, the online landscape and the world of data collection, management and analysis were much less sophisticated. Since then, many startups have risen while others have faltered, and those that have found success were largely companies that were able to adapt to an increasingly data-driven marketplace. Today, our users generate a staggering 10 terabytes of data every single day. On a global scale, PicsArt has a medium- to large-size big data cluster with most of the large-size cluster functionalities enabled.

It was evident that we were stepping into the big data arena when our data met all four characteristics of big data: volume, velocity, variability and complexity. Once the volume of data we were dealing with was too large to fit into a relational or other standard database, the die was cast and we jumped into the big data scene with optimism and gusto. Besides that, because AI and machine learning became a mainstream technology, we were able to fully use it to benefit our users.

Adapting To A Big Data Mindset

When most people think about big data, they often imagine that the technical side would be the most difficult, but we found out through trial and error that approaching problems from a technical side first isn’t always ideal. Big data offers nearly endless possibilities, but if you don’t have a clear understanding of specific use cases and goals, you can unnecessarily prolong the development process. Since our system was constructed without a clearly delineated list of use cases, our data architects had to design it to handle as many future use cases as possible. The end result was a working system, with extensive support and capabilities, but the rollout time was longer than it could have been had we defined things better from the start.

Getting used to the sheer scope of data was a learning curve as well, especially since there was a lack of a big data community at the time. Initially, we placed responsibility for cleaning data on a single centralized team, which we quickly discovered would never work due to the constant barrage of thousands of events happening across multiple apps. Getting the data clean, we discovered, requires simultaneous efforts from the tech and business teams — it only works if everyone is on the same page. Big data is considered the new oil nowadays, but it’s also a huge challenge in terms of how to prepare it, process it, store it and most importantly, turn it into applicable knowledge. To make that happen, it’s important to define the most common use cases within the product and align technical and business team efforts from the beginning. Overall, maintaining flexibility, learning from mistakes and adapting was essential to getting past the first step to becoming a big data company.

Finding The Right Tools For The Job

As the value of big data became more evident, conferences started popping up, giving innovators and companies a way to gather and share strategies. Open source solutions for data analysis and collections became more common and more robust, and it got easier to find the right technology. The lesson that start-ups can take away from all this is to take advantage of the big data community that exists now and do so with direct aims in mind.

In a wide range of tools, it’s really important to find those that fit your business needs. That can be done only empirically depending on the size of your company. It is important to discover tools for data processing, data analysis, crash monitoring and infrastructure monitoring.

Using Data To Fuel Innovation

Each piece of data my company collects falls into one of three categories: user device info, user behaviour and uploaded images — complete with editing logs and intermediate steps. The metadata we collect is used to directly improve the user experience by responding to the way people use our app and then creating the tools they want.

Privacy is definitely an important topic for every tech enterprise that deals with a large amount of data. As an organization operates globally with data on citizens in European Union countries, they must comply with strict new rules around protecting customer data: The General Data Protection Regulation sets a new standard for consumer rights regarding their data. All of our users have the opportunity to adjust their preferable privacy settings and make sure they are comfortable with the data they are sharing with us.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Hovhannes Avoyan.

Big data is undoubtedly one of the hottest trends of our age, and the promise of enormous amounts of data to fundamentally transform how our organizations operate is considerable. For many however, the promise remains just that, with numerous barriers holding them back, whether it’s a lack of board level buy-in or poor quality data.

Arguably the most substantial drag on our efforts however has been a lack of skills. It’s a situation that is likely to see companies aim to triple the size of their data science teams in the next few years. That’s the finding of a recent paper from ESADE researchers.

The researchers examined over 100 Spanish companies from across a range of sectors, most of which had over €200 million in turnover. The results revealed the long way we still have to go before data is at the heart of organizational behaviour.

Slow progress

Despite big data being technologically feasible for several years, over half of the organizations revealed that they are yet to have a culture of data-based decision making, whilst 40% admitted that they don’t have a specific leadership role for data.

This reticence is important, as the study found that companies with a more analytical culture performed better than those without. This was reflected in both their financial performance and the perception of staff at the companies. Indeed, some 78% of companies who were regarded as very analytical thought that this culture had a significant impact upon their performance.

The study found that data professionals tended to fall into one of two categories:

Data scientists, who tend to perform advanced analyses.

Data managers, who provide the business vision to connect these analyses to the strategy of the business.

The typical data team would have between 5 and 20 members, but pretty much every organization reported finding it difficult to find the talent they needed. Despite these recruitment challenges, the majority of organizations wanted to considerably increase the size of their data teams in the next three years, with three times as many data scientists and 2.5 times te number of data managers.

Train or recruit?

The desire for data science skills is clear, but this study suggests that most companies want to hire in external talent, or in other words the finished article. This strategy would be fine except by all accounts, that talent isn’t currently existing in the marketplace, so there appears to be an inherent hope that external bodies will train people for them.

A post was written previously about a similar issue when it comes to artificial intelligence skills, and data science and AI are so intertwined that the same surely applies.

Rather than attempting to hire in the finished article in an increasingly barren marketplace, companies are surely better off investing in data-science training and therefore upgrading their existing talent pool. This approach has numerous advantages, not least of which is raising data skills across the board at a time when a growing number of organizations are attempting to democratize data science capabilities across the workforce rather than concentrate it within a data science function.

Organizations can achieve quick initial results by identifying employees with existing programming, analytical and quantitative skills and augmenting them with both the latest data-science skills and access to powerful tools, such as Python and Hadoop.

Spreading the availability of data education across the business, into marketing, finance, engineering and various other functions provides data literacy to people from various backgrounds. This in turn will help to spread the data-driven culture that data advocates so crave.

A good example of this in practice is the Data University that Airbnb have created to provide anyone who wants to learn about data an opportunity to do so. Already the company has trained over 500 (or 1/8th of the workforce) employees, with dividends already being reaped in the shift towards data-based decision making.

There has never been a better time to invest in the skills and talents of your workforce, with data promising to transform functions and processes throughout organizations that are already experimenting with a range of data science and machine learning initiatives. Expertise is the principle barrier holding these back, so now really is the time to invest in the training that will bridge that gap.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Adi Gaskell.

SAP has formalized its approach to Customer Relationship Management (CRM) by consolidating upon recent acquisitions and integrating these functions into its existing stack of database and data analytics technologies.

In specific terms, SAP has now brought together acquisitions including Hybris (acquired in 2013 – CRM and commerce software), Gigya (acquired in 2017 – customer identity management technology used to manage customer profiles, preferences, opt-ins and consent settings) and CallidusCloud (acquired in 2018 – technology that links salespeople with information related to pricing, incentives & commission all linked to a firm’s Enterprise Resource Planning (ERP) systems) — the combined sum of these parts will now be known as SAP C/4HANA.

As Forbes writer Bob Evans notes here, the overall technology proposition here is a direct play (Evans calls it a ‘head on assault’) at Salesforce, but with what SAP claims to be a more holistic connection to an enterprise’s deeper software stack and ERP systems, SAP’s bread and butter. This makes it CRM engineered more directly into a business supply chain. If you believe the marketing, this is what all the vendors like to call a 360-degree view of the customer.

With SAP’s existing business suite being labelled SAP S/4HANA, the firm has obviously adopted the same naming convention replacing the S-for suite with C-for CRM. The company’s drive to build a more established CRM offering will see it go head to head against not just Salesforce, but a selection of established players in this space including Oracle, Dynamics 365, Verint, Pegasystems and others.

Customer Experience division (the new SAP grouping that includes Hybris, Gigya, Callidus and other elements) president at SAP is Alex Atzberger. Suggesting that there have been four eras of CRM through the ages, Atzberger details them as:

Basic customer sales-based lead optimization systems.

So-called ‘point’ solutions designed to address one specific CRM issue.

Cloud-based systems.

More intelligent holistic connected CRM systems that connect the customer experience to the actual supply chain that an enterprise operates on a day-to-day basis.

Lamenting what SAP CEO Bill McDermott has called the “sales-only focus of legacy CRM solutions”, SAP thinks it can offer a new notion of CRM that exists in the 4.0 age. This is CRM that is more intrinsically engineered into (and integrated with) a customer’s wider software stack of applications and database management systems – and indeed the enterprise demand and supply chain.

“SAP was the last to accept the status quo of CRM and is now the first to change it,” said McDermott. “The legacy CRM systems are all about sales; SAP C/4HANA is all about the consumer. We recognize that every part of a business needs to be focused on a single view of the consumer. When you connect all SAP applications together in an intelligent cloud suite, the demand chain directly fuels the behaviours of the supply chain.”

In line with its new CRM offering SAP has also announced the SAP HANA Data Management Suite. This is software designed to combat what has been called ‘data sprawl’ resulting from firms who operate with highly distributed data that exists in lots of different locations, on different devices, on different platforms, in different states (structured, semi-structured and unstructured) and in different business workflows and business processes.

The SAP C/4HANA suite will offer full integration with SAP’s business applications portfolio, led by the SAP S/4HANA ERP suite.

Crowd-service: more help, from ‘any’ employee

There’s one other add on here for customer service. SAP has also acquired Switzerland-based Coresystems AG to improve field-service customer experience, especially in the manufacturing, energy, high-tech and telecommunications industries. This software service provides scheduling for customer-service requests and uses artificial intelligence-powered crowd-service technology. SAP insists that this broadens the ‘service technician pool’ (those people able to fix any particular problem that occurs in a company during its working day) to include company employees, freelancers and industry partners. The ‘crowd service’ concept means that enterprises can assign the best-qualified technician (or person able to help) to each service call by taking into account expertise, location and availability.

“All systems rely on data, yet the challenge facing companies today is distributed data — data that is not just in transactional systems but scattered across products, machines and people. It is about data that must be ingested, prepared and made enterprise relevant. SAP HANA Data Management Suite enables enterprises to turn massive amounts of data — both structured and unstructured — into valuable, usable knowledge, no matter where it resides,” notes SAP, in a product launch statement.

The SAP Hybris name (along with other acquired firms noted in this story) will now be retired to consolidate under the SAP Customer Experience business unit.

The real challenge here is…

Whether the next generation of CRM actually results from one vendor firing pot-shots or thinly-veiled swipes at one another or not, the big question here will come down to implementation, integration and interconnectedness of the systems being built.

As already suggested here, success in the 360-degree connected CRM world is a question of real end-to-end real-time synchronization between the demand chain and supply chain. That means using ERP and CRM — and a list of other favourite tech industry acronyms including Field Service Management (FSM), Human Capital Management (HCM), IT Service Management (ITSM) and more – and being able to access the data that resides in the clouds serving those functions.

Unless we the humans can get access to the right data, in the right cloud services, serving the right business processes, in the right configuration patterns… then we won’t be able to physically get our developers to code the right functional ‘scripts’ into the codebases that run the so-called ‘smart’ (CRM or otherwise) applications of the future.

There’s a gap in between pure theory and applied empirical success here and SAP will obviously now be working hard to make sure it has customer reference points to convince us that its vision holds water. Claims that CRM is dead and that we can now shout long live 360-degree ERP CRM require deeper analysis and the journey is just starting. This revolution will be televised.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Adrian Bridgwater.

Artificial intelligence’s role in marketing has grown immensely over the past year– and we have only started to scratch the surface of its mammoth impact. AI is quickly (and permanently) changing marketing as we know it, and creative marketers must adapt to deal with the ultimate consumer bodyguard.

As virtual assistants get to know their owners better than they know themselves, they will be able to not only make product recommendations but carry them out without asking their owners. For example, within the next few years, a virtual assistant will know its owner’s lifestyle, preferences, financial position, risk appetite, personality makeup and schedule so well that it will do the grocery shopping, trade stocks, purchase new products, buy books and schedule appointments — without ever asking for permission.

While it may be scary to think about, this new reality is just around the corner. Marketers need to start acting today for this mega-disruption because marketers will soon have to market more to virtual assistants and less directly to customers.

A Matter Of Trust

But how could anyone ever give AI that much leeway over their life and choices? One word: trust. We all know that trust is earned, not given. As Alexa and Bixby start to make their owners’ busy lives easier, happier and more efficient (not to mention saving them time and money), consumers will trust their virtual assistants with more and more decisions. Here’s why marketers must act creatively and decisively: People will start trusting their virtual assistant more than brands.

We’re all aware of the sharp decline of consumer trust in CEOs and brands over the past few years. According to the 2017 Edelman Trust Barometer, the credibility of CEOs fell by 12 points last year to 37% globally, and just 52% of respondents said they trust businesses. According to a 2017 McCann survey, 42% of Americans find brands and companies less truthful today than 20 years ago. Talk about perfect timing for AI to fill the trust void in business leaders and brands today.

Taming the Beast

How can marketers work with and circumnavigate the AI beast (there is no slaying it) that stands between them and their customers?

If you can’t beat ‘em: Marketers should never surrender to AI. Instead, we must work with this gatekeeper. Virtual assistants will accept money from companies that want to have a voice on their platforms. This represents the transition from marketing directly to your customer base to marketing to virtual assistants in the very near future.

Messaging and imagery: These two aspects, especially when shared through video, are the most powerful and convincing tools in the modern marketer’s toolbox. Do not underestimate the role that emotion plays in the decision making process, regardless of channel, format or audience.

Communicating trust: In the future, imaginative marketers and designers can put their companies, brands and messages above virtual assistants by communicating that, although there is nothing wrong with hearing out Alexa, it is not the be all and end all. Human interaction will always trump technology and should be more trusted.

Human interaction: Face-to-face interactions between marketers and their customers should be occurring frequently, and this will be even more critical in the future as AI digs in as the gatekeeper to consumers. The nature, tone and outcome of a conversation with a marketing professional is entirely different from that with a sales professional. Marketers need to step up their interactions to not only learn what makes customers tick but to be the human interaction in an increasingly non-human world.

Upcoming Trends

AI is going to torch just about every part of marketing, branding and the buyer journey as we know it, forcing marketers to completely rethink everything they do if they want to stay competitive in the coming years. Below are four trends we will likely see in the coming few years as a result of AI innovation.

Decline in SEM: Text ad spending will decline sharply with the rise of virtual assistants and voice-based searches. A January 2018 Forbesarticle referenced a report that found that voice searches increased 35-fold between 2008 and 2016. Marketers will spend much less on traditional SEM in the future and more on virtual assistant platforms to ensure their brands are represented.

Increase in direct mail: What better way to combat today’s technology than with yesterday’s workhorse? However, dated marketing methods (e.g., postcards and envelopes) need a refresher to remain relevant. To get noticed amidst the junk mail, direct mail needs to stand out. Think creatively designed boxes that beg to be opened filled with cool, personalized stuff (for example, an inexpensive audio gadget that plays a message from the company CEO), non-sales messaging and directions to download the company’s latest virtual reality experience.

Decline in traditional research: Marketers will collect most of their (non-face-to-face) customer research from virtual assistants in the near future, providing valuable demographic, psychographic and lifestyle information on their target markets. Web analytics, surveys and social media data will still be valuable but will not be able to provide the same rich data that virtual assistants offer. This type of research will be expensive but necessary to help paint the most accurate picture (or buyer persona) of your customers.

Marketers’ jobs will become even more challenging in the coming years, forcing them to engage their customers more frequently to build better human relationships in a less human world.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Christian DeGobbi.